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SAR and QSAR in Environmental Research Volume 22, 2011 - Issue 3-4
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Original Articles

Androgen receptor binding affinity: a QSAR evaluation

M. Todorov Laboratory of Mathematical Chemistry, Bourgas As. Zlatarov University, Bourgas, Bulgaria
,
E. Mombelli Unité Modèles pour l’Ecotoxicologie et la Toxicologie (METO), Institut National de l’Environnement Industriel et des Risques (INERIS), Verneuil en Halatte, France
,
S. Aït-Aïssa Unité Ecotoxicologie in vitro and in vivo (ECOT), Institut National de l’Environnement Industriel et des Risques (INERIS), Verneuil en Halatte, France
&
O. Mekenyan Laboratory of Mathematical Chemistry, Bourgas As. Zlatarov University, Bourgas, BulgariaCorrespondence[email protected]
Pages 265-291 | Received 09 Jul 2010, Accepted 28 Sep 2010, Published online: 18 May 2011

Citations (17)

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F. Lunghini, G. Marcou, P. Azam, F. Bonachera, M.H. Enrici, E. Van Miert & A. Varnek. (2021) Endocrine disruption: the noise in available data adversely impacts the models’ performance. SAR and QSAR in Environmental Research 32:2, pages 111-131.
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U. Norinder, A. Rybacka & P.L. Andersson. (2016) Conformal prediction to define applicability domain – A case study on predicting ER and AR binding. SAR and QSAR in Environmental Research 27:4, pages 303-316.
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J. Devillers, E. Bro & F. Millot. (2015) Prediction of the endocrine disruption profile of pesticides. SAR and QSAR in Environmental Research 26:10, pages 831-852.
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J. Devillers, J.P. Doucet, A. Doucet-Panaye, A. Decourtye & P. Aupinel. (2012) Linear and non-linear QSAR modelling of juvenile hormone esterase inhibitors. SAR and QSAR in Environmental Research 23:3-4, pages 357-369.
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S. Kovarich, E. Papa, J. Li & P. Gramatica. (2012) QSAR classification models for the screening of the endocrine-disrupting activity of perfluorinated compounds. SAR and QSAR in Environmental Research 23:3-4, pages 207-220.
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Articles from other publishers (12)

Mark Stanojević, Marija Sollner Dolenc & Marjan Vračko. (2023) Predictive Models for Compound Binding to Androgen and Estrogen Receptors Based on Counter-Propagation Artificial Neural Networks. Toxics 11:6, pages 486.
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Tianshi Yu, Chanin Nantasenamat, Supicha Kachenton, Nuttapat Anuwongcharoen & Theeraphon Piacham. (2023) Cheminformatic Analysis and Machine Learning Modeling to Investigate Androgen Receptor Antagonists to Combat Prostate Cancer. ACS Omega 8:7, pages 6729-6742.
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Arkaprava Banerjee, Priyanka De, Vinay Kumar, Supratik Kar & Kunal Roy. (2022) Quick and efficient quantitative predictions of androgen receptor binding affinity for screening Endocrine Disruptor Chemicals using 2D-QSAR and Chemical Read-Across. Chemosphere 309, pages 136579.
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Sean P. Collins & Tara S. Barton-Maclaren. (2022) Novel machine learning models to predict endocrine disruption activity for high-throughput chemical screening. Frontiers in Toxicology 4.
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Alfonso T. García-Sosa & Uko Maran. (2021) Combined Naïve Bayesian, Chemical Fingerprints and Molecular Docking Classifiers to Model and Predict Androgen Receptor Binding Data for Environmentally- and Health-Sensitive Substances. International Journal of Molecular Sciences 22:13, pages 6695.
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Marina P Savić, Ivana Z Kuzminac, Dušan Đ Škorić, Dimitar S Jakimov, Lucie Rárová, Marija N Sakač & Evgenija A Djurendić. (2020) New oxygen-containing androstane derivatives: Synthesis and biological potential. Journal of Chemical Sciences 132:1.
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Aleksandra Rybacka, Christina Rudén, Igor V. Tetko & Patrik L. Andersson. (2015) Identifying potential endocrine disruptors among industrial chemicals and their metabolites – development and evaluation of in silico tools. Chemosphere 139, pages 372-378.
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Claudia Bobach, Stephanie Tennstedt, Kristin Palberg, Annika Denkert, Wolfgang Brandt, Armin de Meijere, Barbara Seliger & Ludger A. Wessjohann. (2015) Screening of synthetic and natural product databases: Identification of novel androgens and antiandrogens. European Journal of Medicinal Chemistry 90, pages 267-279.
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Nikolai G. Nikolov, Marianne Dybdahl, Svava Ó. Jónsdóttir & Eva B. Wedebye. (2014) hERG blocking potential of acids and zwitterions characterized by three thresholds for acidity, size and reactivity. Bioorganic & Medicinal Chemistry 22:21, pages 6004-6013.
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Wei Wang, Wanlin He, Xi Zhou & Xin Chen. (2013) Optimization of molecular docking scores with support vector rank regression. Proteins: Structure, Function, and Bioinformatics 81:8, pages 1386-1398.
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Xiaolin Li, Li Ye, Wei Shi, Hongling Liu, Chunsheng Liu, Xiangping Qian, Yongliang Zhu & Hongxia Yu. (2013) In silico study on hydroxylated polychlorinated biphenyls as androgen receptor antagonists. Ecotoxicology and Environmental Safety 92, pages 258-264.
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. (2012) Current World Literature. Current Opinion in Endocrinology, Diabetes & Obesity 19:3, pages 233-247.
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